AI & ML Research 3 Days

Last updated: June 25, 2026, 2:30 PM ET

AI Research & Development

Google Deep Mind introduced computer use capabilities for its Gemini 3.5 Flash model, marking a significant step in multimodal AI. This development follows Google AI's research into how reasoning unlocks parametric knowledge in Large Language Models (LLMs), suggesting improved comprehension and knowledge retrieval. Concurrently, OpenAI is actively contributing to global cooperation by helping build shared standards for advanced AI, focusing on evaluation frameworks and safety practices through initiatives like the Appia Foundation. These efforts aim to ensure responsible development and deployment of increasingly sophisticated AI systems.

LLM Architectures & Applications

Towards Data Science explored the "Arbiter Pattern" for Retrieval Augmented Generation (RAG), where one LLM call ranks candidate pages with justifications, providing auditable outputs. This approach, detailed in their Enterprise Document Intelligence series, aims to refine how LLMs access and utilize external information. Complementing this, Towards Data Science further segmented RAG by proposing parallel detectors for anchor detection, followed by a final LLM call, suggesting a structured filtering process for retrieval. Google AI Blog also shed light on LLM recall mechanisms, revealing through activation patching in Gemma models that the residual stream handles most of the factual recall work, offering insights into how these models store and access information.

AI Agents & Workflow Automation

OpenAI published research detailing how AI agents are transforming work by enabling longer, more complex tasks and boosting productivity across various roles. This aligns with practical applications discussed on Towards Data Science, where a user shared their experience building a multi-agent pipeline instead of relying on a single agent, using text-to-SQL as a demonstration. Further exploration into agent capabilities comes from Towards Data Science, which provided guidance on creating powerful loops within Claude code for coding agents. For developers looking to build local AI coding agents, Towards Data Science offered a step-by-step guide using Gemma 4 and Open Code, detailing the setup from Ollama installation to launching Open Code with a local model.

Data Engineering & AI Integration

The evolution of data engineering in the age of AI was a recurring theme. Towards Data Science featured a reflection on the first month of learning data engineering publicly, highlighting the motivations and actual challenges faced. A practical onboarding workflow for new data engineers was presented on Towards Data Science, focusing on making ETL pipelines testable through environment setup, automated testing, and AI-assisted development. In a different context, Towards Data Science shared an experience where Gemini solved a Pandas data preprocessing task in seconds, underscoring the efficiency gains while also noting the continued importance of data science fundamentals.

Chip Technology & AI Infrastructure

IBM has unveiled a new prototype chip technology that could potentially extend Moore's Law for another decade, boasting approximately 100 billion transistors on a fingernail-sized area, doubling the density of their previous leading-edge technology. This advancement in chip manufacturing is critical for powering the next generation of AI systems. In a related development, OpenAI announced a collaboration with Broadcom to introduce Jalapeño, a custom AI chip specifically designed for LLM inference, aiming to enhance performance, efficiency, and scalability. MIT Technology Review AI also highlighted a $400 million machine crucial for the future of chipmaking, a massive piece of equipment central to advanced semiconductor fabrication.

AI in Specific Industries

The impact of AI on various sectors is becoming increasingly apparent. In retail, MIT Technology Review AI suggested that AI's most significant transformations may not be consumer-facing features but deeper operational shifts. OpenAI detailed how Omio is leveraging their technology to build conversational travel experiences, accelerate product development, and transition into an AI-native company. Furthermore, OpenAI shared a case where GPT-5 Pro assisted an immunologist in solving a three-year-old mystery related to T cell behavior, potentially aiding research in cancer and autoimmune diseases.

AI Safety & Ethics

OpenAI is actively involved in building shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation. This focus on safety and responsible development is crucial as AI capabilities expand. The potential of AI to address global challenges was also touched upon, with MIT Technology Review reporting on AI warning systems being developed in India to help avoid deadly clashes between humans and elephants, a significant conservation effort.

Foundational AI Research

Research into the fundamental workings of LLMs continues. Google AI Blog explored how reasoning unlocks parametric knowledge within LLMs, offering a deeper understanding of their internal processes. Towards Data Science provided an analysis of factual recall circuits in Gemma models, using activation patching to reveal how facts are stored, routed, and read out across transformer layers, noting the residual stream's significant role. This line of inquiry aims to demystify LLM behavior and improve their reliability.

No-Code AI & Developer Roles

The rise of no-code AI platforms is changing the landscape for programmers. Towards Data Science discussed the era of no-code AI, acknowledging that many programmers may feel their specialized skills are becoming less distinct. This trend suggests a shift in how AI tools are accessed and utilized, potentially democratizing AI development and application.

AI in Scientific Discovery

Beyond specific industry applications, AI is accelerating scientific discovery. MIT Technology Review reported on engineered "mini livers" that could potentially be injected as an alternative to transplantation, a development stemming from research by Professor Sangeeta Bhatia. In a different scientific vein, MIT engineers found the first direct evidence that plant seeds can sense sound, with rice seeds germinating significantly faster when exposed to specific water vibrations. This points to AI's growing role in uncovering fundamental biological and physical phenomena.

AI and Information Retrieval

The efficiency and accuracy of information retrieval systems are being significantly improved by AI. Towards Data Science presented a mental model for enterprise RAG, framing retrieval as a filtering process rather than pure search, advocating for filtering based on structured data like tables and table of contents before employing embeddings. This structured approach aims to optimize how AI systems access and synthesize information from large datasets, a critical component for many AI applications.

AI for Health & Well-being

AI is also being applied to address health challenges. MIT Technology Review detailed a breath test being developed that could diagnose pneumonia and other lung conditions in minutes using a portable, chip-scale sensor. Additionally, OpenAI is backing an initiative alongside Stripe and Anthropic to combat respiratory infections, highlighting a concerted effort to leverage AI for public health. The broader impact of AI on mental health was also addressed, with discussions on opening doors to online mental health support, acknowledging the need for accessible coping strategies and vocabulary for emotional well-being.